Project Summary Colorectal carcinoma is the third most commonly diagnosed cancer and the second leading cause of death from cancer in the United States. Often it is diagnosed at an advanced stage, after the patient has developed symptoms, explaining its high mortality rate. Since most colon cancers arise from polyps over a 5 to 15 year period of malignant transformation, screening programs to detect small polyps have been advocated. Unfortunately most people do not follow this recommendation. The health relatedness of this project is to dramatically increase the number of people willing to participate in screening programs by developing a convenient, nearly risk-free method of detecting small polyps. Computed tomography colonography (CTC) or CT-based virtual colonoscopy (VC) has shown a comparable performance to the gold-standard optical colonoscopy (OC) for detecting polyps of 8mm and larger by a less invasive manner. To be a screening tool, current CTC has to be advanced for acceptance by a larger population with higher detection capability on smaller polyps. These needs are documented by the recent refusal of CTC being accepted by Medicare coverage, such as reducing CT radiation, detecting smaller polyps, relieving bowel preparation (BP) stress, minimizing reader variation, improving efficiency, etc. We have made good progress in technical advancement toward the broad, long-term objective of this project, i.e., developing CTC as a safe, accurate, cost-effective, minimal-invasive, least-stressful screening modality. To advance CTC toward the long-term objective, the specific aims of this project renewal are: (1) To develop and evaluate an adaptive statistical reconstruction algorithm to obtain the current abdominal CT image quality at as low mAs level as achievable to minimize the risk of X-ray exposure. The algorithm will consider not only the X-ray quanta statistics and energy spectrum, but also the system background noise because this noise plays a noticeable role at low mAs level. (2) To develop and evaluate a partial-volume (PV) statistical segmentation algorithm with correction of fecal tagging inhomogeneity to improve electronic colon cleansing (ECC). The improvement will lead to the detection of smaller polyps, reduction of CT radiation by a half, and relief of patient stress on BP by approaching toward cathartic-free CTC. (3) To develop and evaluate an ECC-adaptive, texture-based, feature-extraction algorithm to improve computer-aided polyp detection (CADpolyp) toward enhancement of CTC cost-effectiveness. It is hoped that an improved performance with higher detection capability on smaller polyps and more acceptable by a large population (via less radiation and stress) would advance CTC toward a screening modality with acceptance by Medicare coverage. The low-mAs statistical reconstruction would be helpful to other CT applications. The PV image segmentation with correction of inhomogeneity would be beneficial to other virtual endoscopy applications, such as extracting blood vessel wall for plaque analysis in virtual angiography and bladder wall for early detection of growth in virtual cystography.